Changes in School Attendance Zones over Time
The effect of segregation on Zoned-in and Zoned-out Areas
Introduction
In the context of geographic segregation, schools play a fundamental role by either attracting or pushing away residents according to different socioeconomic characteristics (Hasan and Kumar 2019; Gibbons, Machin, and Silva 2013; Figlio and Lucas 2004). In that sense, the opening and closure of schools can lead to dramatic transformations in neighborhoods for current residents and families attracted to these new areas.
In this paper, we examine some of the core structural segregation that can arise or increase when there are changes in school attendance zones over time in Texas, zoning certain areas to better schools compared to others. The objective is to improve our understanding of what happens to existing schools and neighborhoods when new schools are built, focusing on the institutions and communities zoned out due to the appearance of a new attendance zone. In particular, we will analyze (i) changes in school composition and performance and (ii) changes in residential zones through housing prices. The central hypothesis of this paper is that in rapidly gentrified neighborhoods, new schools are more likely to appear to satisfy the demand of new residents for their “own” school. This phenomenon can lead to increased segregation at the school and neighborhood levels.
This paper will contribute to the literature on school and residential segregation and the complementarities of both phenomena on students and neighbors. In terms of school segregation, there has been an important focus on the effects of racial and socioeconomic disparities within schools and their impact on future outcomes (Reardon 2016; Billings, Deming, and Ross 2014). A relevant contribution of this paper is that we will be able to assess a school’s performance under different levels of segregation over time, leveraging the sorting that occurs when some students leave their original school to attend a new one. This project will also speak to the peer effects literature more broadly, assessing potential cream-skimming effects that occur with the introduction of new schools (Denning, Murphy, and Weinhardt 2021; Altonji, Huang, and Taber 2015; Dills 2005) . Regarding neighborhood segregation, authors have extensively shown that the characteristics of the neighborhood you live in affect different socioeconomic and labor outcomes for parents and their children (Chetty et al. 2020). If the introduction of a new school affects housing prices in a specific area, this change could increase or decrease socioeconomic and racial segregation. Given the geographic nature of the data we will be working with, it provides a robust setup to analyze changes in housing prices over time using variations in the attendance zones in general and leveraging the discontinuity in the newly created boundaries. At the same time, the availability of housing prices allows us to identify gentrification zones that could be more affected by the introduction of new schools.
Regarding geographic data, the use of attendance zones or district boundaries has been previously tackled in the literature to assess the effect of school characteristics on housing prices and students’ outcomes (Black 1999; Billings, Deming, and Ross 2014; Figlio and Lucas 2004). Our strategy follows some of this research, using repeated sales in properties over time to assess valuation with a specific focus on school composition. With this structure, we can assess how introducing a new school, which might not even be ranked yet, affects parental preferences, reflects on property prices, and impacts academic performance.
Data
The data used for the analysis corresponds primarily to administrative data collected from multiple sources and harmonized as a large panel of schools and neighborhood characteristics between 2005 and 2019 in Texas. We combine data from Common Core of Data (CCD) and the Texas Education Agency (TEA) to characterize schools, in addition to Census tract level data and housing prices from CoreLogic to characterize neighborhoods over time.
Finally, we are collecting data for attendance zones over time for the entire state, which will allow me to identify the areas that are directly affected by the introduction of a new school, as well as the areas affected by redrawing their original attendance zone boundaries.
Methodology
To study this problem, we will use a combination of Augmented Synthetic Control Method (ASCM) (Ben-Michael, Feller, and Rothstein 2021) and Synthetic Difference-in-Differences (SDD) (Arkhangelsky et al. 2021) to build sound counterfactuals, but also contributing methodologically to the literature by adapting these methods to account for spillover effects. In addition to a synthetic control approach, we will leverage the discontinuities at the boundaries of new attendance zones. Combining a difference-in-differences (DD) approach with a regression discontinuity design (RDD) provides an identification strategy that is stronger in terms of internal validity (Butts 2021; Bennett 2020), and could serve as an additional robustness check for previous strategies.
Given the political nature of some of the boundaries in catchment areas, where shifts in attendance zones potentially coincide with other changes, a sensitivity analysis to hidden bias is necessary to assess the robustness of our findings. The work of Keele, Small, and Foggarty (2019) and Yin et al. (2022) will inform sensitivity analysis to potential unobserved confounders in this setting, providing another methodological contribution on how to adapt these methods created for traditional difference-in-differences settings to more complex scenarios of SDD and ASCM in the presence of spillovers.
Preliminary Results
Measuring segregation
In this section, we show some results for the evolution of the gap on test scores between white and African-American students for different levels.
All schools
For this analysis, we use all districts in Texas with at least 5 regular schools (public schools, excluding charter and magnet schools). We construct a panel of districts available between 2010 and 2018, comparing those that built one new school between 2013 and 2016^[Schools that built more than one school or built a new school outside between 2010 and 2012 or 2017 and 2018 were excluded from the analysis]. We use an Augmented Synthetic Control Method (ASCM) approach leveraging the staggered adoption of the introduction of new schools, and analyze the evolution of the gap between white students and African American students. Additionally, we control for additional covariates such as: total number of schools in the district, average enrollment per school, average percentage of free or reduced price lunch students, average percentage of Hispanic, Asian, and African American.
Figure 1 and Figure 2 show the results for the gap between white and African American students for the top and the bottom of the proficiency distribution. As it can be seen from these figures, the main differences are happening at the top of the distribution, were the gap between white and Black students is increasing.
High schools
In order to adjust for the introduction of different levels of schools have different effects, we conduct the same exercise as in the previous section but only using high schools.
New high schools in Texas
The following map Figure 4 shows all the districts that had at least one new high school in the period between 2010 and 2018. In this interactive map, the year indicates the school locations, identifying high schools that opened that year. Additionally, we can analyze changes in attendance zones by comparing attendance zones in the year 2009-2010, 2013-2014 and 2017-2018.